Querying Dynamics in Evolving Graphs and Networks

查询演化图和网络中的动态

• 批准号: RGPIN-2017-05790
• 负责人: Pei, Jian
• 金额: $ 3.06万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634774
• 关键词: Querying; Dynamics; Evolving; Graphs; Networks

In many applications, such as social network analysis, fraud detection, and network quality diagnosis, a large amount of complex data are modeled as graphs or networks. Graphs and networks enable us to analyze sophisticated behavior that cannot be understood comprehensively in the past, such as influence propagation and group collaboration in a social network. Such dynamics are a beauty in graph and network data. Moreover, due to the scale and the complexity of data modeled as graphs and networks, changes and evolutions are inevitable. Analyzing and making good use of dynamics in evolving graphs and networks, on the one hand, provide us unprecedented power and tools to conquer big data, and, on the other hand, post grand technical challenges. This proposed research is to embrace the opportunities and address the technical challenges.

在许多应用程序中，例如社交网络分析，欺诈检测和网络质量诊断，大量复杂数据被建模为图形或网络。图形和网络使我们能够分析过去无法全面理解的复杂行为，例如在社交网络中的影响力传播和群体协作。这种动态是图和网络数据中的美。此外，由于图形和网络建模的数据的规模和复杂性，变化和演变是不可避免的。一方面，在不断发展的图形和网络中分析并充分利用动力学，为我们提供了前所未有的力量和工具来征服大数据，另一方面，在巨大的技术挑战中提出了大量的大数据。这项拟议的研究是拥抱机会并应对技术挑战。